Long-term monitoring of landbird populations within the National Park Service (NPS) North Coast and Cascades Inventory and Monitoring Network (NCCN) began in 2005, with the goal of detecting trends to inform the conservation and management of landbirds and their habitats. Here we use 2005–2016 data from over 3500 point-count stations to report landbird occurrence and trends in each of five NCCN parks, including three national parks in mountain wilderness areas (Mount Rainier National Park, North Cascades National Park Complex and Olympic National Park) and two historical parks (Lewis and Clark National Historical Park and San Juan Island National Historical Park). Recent advances in point-count modeling were applied to characterize population trends for 68 landbird species, including up to 41 species in each park. Fitted models suggest that almost all species exhibited stable or increasing trends over the study period. Notable exceptions were a decline in the Olive-sided Flycatcher in two parks and single-park declines in the Norther Flicker, Hutton’s Vireo, Clark’s Nutcracker, Mountain Chickadee, Wilson’s Warbler and Dark-eyed Junco. Negative effects of precipitation-as-snow were supported in over one-third of our population models. Lower precipitation-as-snow in the mountain parks might have contributed to rising landbird densities during the study period. Population density also varied with elevation in mountain parks, but temporal trends were similar among elevational strata for each species analyzed, suggesting no evidence of elevational range-shifts during this study. These results reinforce recent analyses of the first 10 years of point-count data from the three mountain parks (Ray et al. 2017 a). In the current analysis, models were extended to explore effects of covariates on species detection probability. Negative effects of ambient noise level on detection were supported in several cases, but adding covariates of detection generally did not lead to substantial improvements in model fit. In some cases, model fit was improved by reducing the scope of inference to a portion of the focal region, suggesting important effects of habitat heterogeneity.